| | ---
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| | language:
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| | - en
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| | license: apache-2.0
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| | tags:
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| | - math
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| | - reasoning
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| | - mathematics
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| | - causal-lm
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| | - text-generation
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| | library_name: transformers
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| | pipeline_tag: text-generation
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| | model_name: Math-A1-2B
|
| | ---
|
| |
|
| | # 🐟 Math-A1-2B
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| |
|
| | **Math-A1-2B** is a 2B-parameter language model by **Kitefish**, focused on mathematical reasoning, symbolic understanding, and structured problem solving.
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| |
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| | This is an early release and part of our ongoing effort to build strong, efficient models for reasoning-heavy tasks.
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| |
|
| | ---
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| |
|
| | ## ✨ What this model is good at
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| |
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| | - Basic to intermediate **math problem solving**
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| | - **Step-by-step reasoning** for equations and word problems
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| | - Understanding **mathematical symbols and structure**
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| | - Educational and experimentation use cases
|
| |
|
| | ---
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| |
|
| | ## 🚀 Quick start
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| |
|
| | ```python
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| | from transformers import AutoTokenizer, AutoModelForCausalLM
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| |
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| | tokenizer = AutoTokenizer.from_pretrained("kitefish/math-a1-2B")
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| | model = AutoModelForCausalLM.from_pretrained(
|
| | "kitefish/math-a1-2B",
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| | torch_dtype="auto",
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| | device_map="auto"
|
| | )
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| |
|
| | prompt = "Solve: 2x + 5 = 13"
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| | inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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| |
|
| | outputs = model.generate(**inputs, max_new_tokens=100)
|
| | print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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| | |
